Methods and systems for compiling marketing information for a client

ABSTRACT

Methods are provided for exploiting the secondary traffic generated by social networking sites. Traffic on a commercial website is constantly monitored by a web analytics tool that collects traffic measurements of hits, button presses, enquiries, purchases etc., as well as the referrer URL of a site, such as a social networking site, through which the commercial website is accessed. The collected measurements are forwarded to a referred traffic analysis system. Concurrently, the referred traffic analysis system crawls the Internet and collects a large number of social networking sites, analyses their content by extracting insight terms and phrases from them. The collected traffic measurements are correlated with the collected insights from the social networking sites, and the top insights that reoccur frequently enough to appear to be a driver for the measurements observed, are presented to the client. A corresponding system is also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/469,026, filed Aug. 26, 2014, which is a divisional of U.S. patentapplication Ser. No. 13/781,635, filed Feb. 28, 2013, (and issued onSep. 23, 2014 as U.S. Pat. No. 8,843,610) which is a divisional of U.S.patent application Ser. No. 13/526,246, filed Jun. 18, 2012 (and issuedon Apr. 2, 2013 as U.S. Pat. No. 8,412,820), which is a continuation ofU.S. patent application Ser. No. 12/819,402, filed Jun. 21, 2010 (andissued on Jul. 24, 2012 as U.S. Pat. No. 8,230,062).

TECHNICAL FIELD

The present invention relates to a computer implemented method andsystem for determining content in social media, and in particular, to acomputer implemented method and system for determining content publishedby a commenter, an individual, or a web site, and determining its effecton referred web sites.

BACKGROUND

The influence of the social media on the effectiveness of commercial websites has become an increasingly important subject. A major problemfacing marketers and public relation professionals revolves around theprolific use of social media sites and their effect in directing trafficto other web sites, for example E-commerce sites and other corporatesites.

Available web analytics software tools, such as Google Analytics andmany other such systems are designed to collect real-time site trafficinformation and can provide resulting statistics through graphical userinterfaces (GUI) and in formatted reports to the operator of E-commerceor other sites instrumented with such web analytics tools.

A brief description of web analytics, provided for example by Wikipedia(http://en.wikipedia.org/wiki/Web_analytics), may be useful to give thereader an introduction to the subject matter:

“Web analytics is the measurement, collection, analysis and reporting ofInternet data for purposes of understanding and optimizing web usage.Web analytics is not just a tool for measuring website traffic but canbe used as a tool for business research and market research. Webanalytics applications can also help companies measure the results oftraditional print advertising campaigns. It helps one to estimate howthe traffic to the website changed after the launch of a new advertisingcampaign. Web analytics provides data on the number of visitors, pageviews etc. to gauge the popularity of the sites which will help to dothe market research. There are two categories of web analytics; off-siteand on-site web analytics. Off-site web analytics refers to webmeasurement and analysis regardless of whether you own or maintain awebsite. It includes the measurement of a website's potential audience(opportunity), share of voice (visibility), and buzz (comments) that ishappening on the Internet as a whole. On-site web analytics measure avisitor's journey once on your website. This includes its drivers andconversions; for example, which landing pages encourage people to make apurchase. On-site web analytics measures the performance of your websitein a commercial context. This data is typically compared against keyperformance indicators for performance, and used to improve a web siteor marketing campaign's audience response. Historically, web analyticshas referred to on-site visitor measurement. However in recent yearsthis has blurred, mainly because vendors are producing tools that spanboth categories.”

A web analytics tool alone can be an important marketing research tool,but while it can provide detailed and summarized information andanalysis of the traffic that arrives at a web site, includingcategorizing sources of the traffic, it cannot determine the reasons forthe traffic arriving.

Accordingly, there is a need in the industry for the development ofalternative and improved methods and systems, which would take intoaccount individual drivers for Internet traffic on commercial web sites.

BRIEF SUMMARY

Methods and systems for compiling marketing information for a client areprovided. Data can be obtained from a plurality of social mediawebsites, where each of the social media websites includes a universalresource identifier that points to a client website. A plurality ofinsights can be extracted from the obtained data, resulting in extractedinsights. Each of the extracted insights comprises text elements thatdenote product approval for at least one product available for sale atthe client website. Measurements of traffic to the client website can becollected, the traffic being referred to the client website by theplurality of social media websites. A referred traffic dynamics summarytable can then be generated based on the extracted insights and themeasurements of traffic. The referred traffic dynamics summary tableaggregates the extracted insights across the plurality of social mediawebsites to rank the extracted insights.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of example,with reference to the accompanying drawings in which:

FIG. 1 illustrates a basic system architecture 100 of the prior art,comprising any number of Referrer Sites 102, a Client Installation 104,a number N of Users 106 (User 1 to User N), and a Web Analytics module108;

FIG. 2 shows a system architecture 200 according to embodiments of theinvention, including a Referred Traffic Analysis System 202;

FIG. 3 shows an exemplary use-case diagram 300 illustrating a series ofevents in the operation of the improved system architecture 200 of FIG.2;

FIG. 4 is an example correlation overview diagram 400 to illustrate theoperation of the Referred Traffic Analysis System 202 of FIG. 2; and

FIG. 5 shows a flow chart 500 summarizing steps performed in theimproved system architecture 200 of FIG. 2.

DETAILED DESCRIPTION

In summary, embodiments of the present invention take availableinformation from a web analytics tool that identifies HTTP referrertraffic, and associate or correlate other events such as visits, sales,downloads, etc. recorded on a commercial web site, with text elementsfound at the referrer site. These text elements (expressions) comprisingfrom one to a few contiguous words, are words or phrases describing, forexample, an attribute of a product for sale at the client site, acomment on quality or otherwise of the product, or other meaningfulterms. In the following, the commercial web site will be called a clientsite, and the text elements will be called “insights”.

FIG. 1 illustrates a basic system architecture 100 of the prior art,comprising any number of Referrer Sites 102, a Client Installation 104,a number N of Users 106 (User 1 to User N), and a Web Analytics module108. The Client Installation 104 includes a Client Website 110 hosted ona web server and a User Interface 112. The referrer sites 102 may besocial media sites, such as a blog, or a news or publication sitescarrying reviews for example.

The Web Analytics module 108 may be provided as an in-house tool withinthe Client Installation 104, but it may also be provided from a serverof an external organization such as Google (seehttp://www.google.com/analytics) or WebTrends (seehttp://www.webtrends.com) which serves multiple clients.

For the purpose of more clearly showing the communication relationships,connectivity over virtual communications links may be described asfollows:

the Users 106 browse to the referrer sites 102 over primary Internetconnections 114;

the referrer sites 102 may include a Universal Resource Identifier (URI)which is effectively a pointer 116 to the Client Website 110;

having acquired a URI, a User 106 (User N for example) may establish asecondary Internet connection 118 with the Client Website 110, i.e. theuser is redirected to the client website;

as the secondary Internet connection 118 is opened a referrer identifier(REF) is transmitted to the Client Website 110 and stored there, REFbeing effectively a pointer 120 to the referrer site 102;

the Client Website 110 may communicate with the Web Analytics module 108over a permanent or periodically established traffic monitoring link122; and

the User Interface 112 may communicate over a permanent or periodicallyestablished analytics connection 124 with the Web Analytics module 108.

In a simple sequence of events, a user, e.g. the User N browses to theReferrer Sites 102, which may be a social media site, such as a blog, ora news or publication site carrying reviews, as indicated earlier.

The user may click on a screen element (e.g. a button, or simply text),which is associated with the URI pointing to the Client Website 110. Asa result, the secondary Internet connection 118 is activated, and theuser interacts now with the Client Website 110.

Activity on the Client Website 110 is monitored over the trafficmonitoring link 122 by the Web Analytics module 108 and recorded. Datarecorded in the Web Analytics module 108 may then be processed, forexample summarized and formatted, and delivered to the User Interface112 over the analytics connection 124, either as a periodic report orinteractively allowing the client to interactively view the data.

What processing in the Web Analytics module 108 of the prior art system100 may also include, is some listing of the Referrer Sites 102 or theirtypes from which the user may have been redirected.

What the embodiments of the present invention add to this scenario, isto provide the client with additional valuable information byextrapolating from the actual content of the Referrer Site 102, combinedwith the activity recorded by the Web Analytics module 108, possiblemotivations of the user, including ranking different Referrer Sites 102as to their value to in bringing business to the client for example, aswell as ranking the “insights” found on the Referrer Sites 102 as totheir effect on the users' reactions.

General Description of the Embodiments

A client has a website (ClientWebsite) and a web analytics tool. Webanalytics tools of various capabilities are commercially available fromcompanies such as Omniture, Meteorsolutions, WebTrends, and others. Theweb analytics tool runs on, or is connected to, the same server as theclient's website.

Activity on the client's website is constantly monitored by the webanalytics tool, which collects statistics of: hits, button presses,enquiries, purchases, etc. as well as the referrer URL of the site thatis accessing the client's website. The collected results of themonitored activity are available to the client in various formats. Thecollected results may also be periodically transmitted in a selectedformat for further processing.

The client is a client of Radian6 Technologies Inc., to be also referredto as Radian6 for brevity, the assignee for this application, whoprovides a service to the client. Briefly, a service provided by Radian6Technologies Inc., the implementation of which is disclosed in theembodiments of the present invention, includes performing additionalanalysis, specifically by associating or correlating the informationgathered by the web analytics tool of the client with third partyinformation gathered by Radian6 Technologies Inc. from publiclyaccessible third party sites, e.g. social media sites on the web,including social networking sites, blogs, reviews, news agencies, andother similar sites which may contain content relative to the client.The primary key upon which correlation to Radian6 data is performed, isvia the referring URL field. However, it is understood that other waysof correlating the collected information are also possible.

Radian6 maintains a database of the aforementioned third partyinformation, which is gathered by a web crawler application periodicallyand speculatively, for use in providing service to clients. Thisinformation may already be available and may have been used in othertypes of service provided by Radian6, and described in earlier patentapplications of the same assignee Ser. No. 12/174,345 filed on Jul. 16,2008; Ser. No. 12/333,277 filed on Dec. 11, 2009 and Ser. No. 12/437,418filed on May 7, 2009, entire contents of the patent applications beingentirely incorporated herein by reference. Some third party informationmay also be included in the web crawler collection activity on demand,as a result of new keywords (trigger or search terms) being requested bythe client.

Not all referrer sites are of interest to Radian6, for example searchsites (Google etc.). Conversely, not all referrer URLs may be known toRadian6, e.g., in-house sites, or referrer sites that were active only ashort time, and were missed by the web crawler.

The client has to manually set up the data integration of their webanalytics tool with Radian6 through a registration process. A typicalexample of service setup requires these fields:

Client Account Name (and Client ID)

Name of the Topic Profile

Application “id” of the report being transferred from the web servicerunning the web analytics tool, to Radian6

API URL that Radian6 will use to request the report

Supply a username/password if required to request the report from theweb service

The time of day that Radian6 should request the report from the webanalytics tool web service (including the time zone)

Names of the measures to be included in the report (up to 10 events)from the web analytics report

-   -   These will also become the names used as the menu options in a        sample screenshot    -   Provide the list of measures in the desired sort order for how        the measures will be sorted in the menus in the Radian6 user        interface    -   Note that Radian6 needs to be notified if the measures in the        report change in order to keep the data load running properly

Domain of the site that measures are being gathered for (i.e.,www.radian6.com)

Multiple domains can be supported

The setup results in defining a database schema, including up to K(K=10) measures of interest to the client. There can be any number ofsets of 10 measures configured in a Radian6 web analytics configuration.It is also understood that K=10 has been selected for convenience, andgenerally K may be higher or lower than 10 as required.

Once a client is set up, their collected set of measurements isperiodically sent in a report to Radian6 and stored in a rollingdatabase that keeps the current and last N reports, for possible trendsanalysis.

Each time a report is received from the web analytics tool of theclient, an SQL (mySQL) program (a data processing job) is run on thecollected measurements in combination with the “insights” collected fromthe referring sites. The outcome of the data processing job for theclient's domain comprises one or more basic correlation records,typically of the form:

key word term, provider URL, client website action (e.g. conversion),count

thus tying social media topics and conversations to specific actions andobjectives on the client's website.

Conversions are defined by the client, depending on the desires of theclient. If the client desires to attract a visitor to the client'swebsite and get the visitor to download a white paper, then that is aconversion. If the client desires to get someone to buy a widget, thenthat is what they define as a conversion.

The basic correlation records are then further processed by Radian6 togenerate various summaries, which are displayed in an interactiveformat, at the client's or any other location.

Detailed Description of the Embodiments

FIG. 2 shows an improved system architecture 200 according to theembodiments of the invention, including the basic system architecture100. But instead of (or in addition to) communicating the results of theWeb Analytics module 108 directly over the analytics connection 124 tothe Client Installation 104, the improved system architecture 200comprises a Referred Traffic Analysis System 202, which receivesformatted analytics data from the Web Analytics module 108 over a webanalytics output connection 204, associates or correlates the receivedformatted analytics data with content gathered from the referrer sites102 by a Web Crawler (a crawler module) 206, and provides thecorrelation results as “referred traffic dynamics” over an interactiveInternet link 208 to the client, i.e., the User Interface 112.

The Web Crawler 206 may be an available computer program or a servicewhich, given a priming source (initial target) and search criteria fromthe Referred Traffic Analysis System 202, will gather many crawled webpages and deliver their content to the Referred Traffic Analysis System202 for further processing.

The Referred Traffic Analysis System 202 comprises one or more computershaving processors, computer storage subsystems, having computer readablemedia such a memory, DVD, CD-ROM or else, network interfaces, andsoftware designed to be executed on said computers, the software havingcomputer executable instructions and data stored on the computerreadable media for execution by a processor, forming various modules ofthe Referred Traffic Analysis System 202 as shown in FIG. 2.

The Referred Traffic Analysis System 202 may simultaneously provide aninsight correlation service to numerous client installations similar tothe Client Installation 104, as well as provide other computationalservices outside of the scope of the present invention. The ReferredTraffic Analysis System 202 may reside in a single location, or it maybe a distributed system with computer and storage subsystems located inseveral locations. In the interest of clarity, only a simplified view ofthe Referred Traffic Analysis System 202, serving a single client (theClient Installation 104) is described in the following. Personsconversant with designing server systems and server applications mayreadily conceive of expanded systems which are within the scope of thepresent invention.

The Referred Traffic Analysis System 202 comprises: a Collector module210, an Insights database 212, an Associator module 214 including anAggregator module 215, an Analytics database 216, a Referred TrafficDynamics (RTF) database 218, and a Presenter module 220 including aGraphical User Interface module 221. Each of these modules comprises acomputer readable program code and/or data stored in a computer readablemedium for execution by a processor.

The Collector module 210 is a software program, stored in a memory, andrunning in one of the computers of the Referred Traffic Analysis System202, which crawls the Internet to find Referrer Sites 102, that issocial networking as well as other sites that include a URI pointing tothe Client Website 110. The Collector module 210 includes an InsightsDictionary 222 of “insights”, that is to say, words or short phrasesthat may indicate for example a value statement, or generally terms thatare commonly used in social networking sites to denote product approval.The Insights Dictionary 222 may also be customized to include termsspecific to the Client Installation 104 such as product names. Asmentioned above, the Insight Dictionary 222 comprises computer readableprogram data stored in a computer readable medium for execution by aprocessor.

The contents of each Referrer Site 102 are compared with the insightslisted in the Insights Dictionary 222, and insights found on eachReferrer Site 102 are stored in the Insights database 212 with theUniversal Resource Locator (URL) of the respective Referrer Site 102.

The Analytics database 216 periodically receives selected analytics datafrom the Web Analytics module 108 over the web analytics outputconnection 204. The selected analytics data stored in the Analyticsdatabase 216 include measurements, i.e., frequency, time, referringlink, etc., of one or more of the following events: visits to the clientwebsite, number of orders of products from the client website, number oforders of products from the client website divided into a category, andnumber of downloads from the client website.

The Associator module 214 is a software program, stored in a memory, andrunning in one of the computers of the Referred Traffic Analysis System202, which combines data from the Insights database 212 and theAnalytics database 216 to periodically generate or update a set ofReferred Traffic Dynamics for storage in the RTF database 218. TheAggregator module 215 of the Associator module 214 aggregates may beused to aggregate the results of the Referred Traffic Dynamics accordingto predefined criteria, for example.

The Presenter module 220 is a software program, stored in a memory, andrunning in one of the computers of the Referred Traffic Analysis System202, which makes the contents RTF database 218 accessible over theinteractive Internet link 208, preferably through the Graphical UserInterface module (GUI) 221, to the User Interface 112 in the ClientInstallation 104. The Presenter module 220 may for example present theRTF data in a website and the interactive Internet link 208 is ahypertext transport protocol (HTTP) link.

The Collector module 210, the Associator module 214, and the Presentermodule 220, may conveniently be implemented as Hypertext Preprocessor(PHP) programs, while the Insights database 212, the Analytics database216, and the RTF database 218 may be relational databases which permitsmany of the database functions such as sorting, filtering, associating,etc., of the software modules (210, 214, 220) to be performed using theStructured Query Language (SQL).

FIG. 3 shows an exemplary use-case diagram 300 illustrating a series ofevents in the operation of the improved system architecture 200,illustrating the interaction between a User-A (302), a Blog “A” 304being an instance of a Referrer Site 102 of FIG. 2 with the URL value“URL_A”, a Product Website 306 which is an instance of the ClientWebsite 110 of FIG. 2, a User-i (308), the Web Analytics module 108, andthe Referred Traffic Analysis System 202 of FIG. 2.

An arrow labeled “writing” from the User-A (302) to the Blog “A” 304indicates that the User-A 302 at some time T1 writes this social mediablog, which includes the written text as well as a Link (URI) to theProduct Website 306.

An arrow labeled “crawl” from the Blog “A” 304 to the Referred TrafficAnalysis System 202 indicates that the Web Crawler 206 delivers the URL(=URL_A) and the content of the Blog “A” 304 to the Referred TrafficAnalysis System 202 where “insights” contained in the Blog “A” 304 areextracted and stored, with the key “URL_A”, in the Insights database212.

An arrow labeled “reading” from the User-i (308) to the Blog “A” 304indicates that the User-i (308) is reading the block at some later timeT2.

An arrow labeled “user click” from the Blog “A” 304 to the ProductWebsite 306 indicates that the User-i (308) has clicked on the Link(URI) in the Blog “A” 304, and thus will be accessing and browsing theProduct Website 306 which includes a Product Description and, forexample, a “buy now” button which will cause the product described onthe Product Website 306 to be bought by the User-i (308).

A set of arrows labeled 122 represent the traffic monitoring link 122from the Product Website 306 to the Web Analytics module 108 whichcollects relevant events on the Product Website 306, specifically theURL=URL_A of the Blog “A” 304 through which the Product Website 306 wasaccessed by the User-i (308), and the conversion event, that is the factthat the User-i (308) has bought the product.

The tracked events on the Product Website 306 due to the visit of User-i(308) are combined by the Web Analytics module 108 into a ReferredTraffic listing 310 of tracked events from other user visits, and sentat a time T3 over the analytics output connection 204 to the ReferredTraffic Analysis System 202 where it is stored in the Analytics database216 (see FIG. 2).

The Referred Traffic report may for example include a statement of theform [new visitor, came from URL_A, bought the product], which may becoded in a standard format such as the JavaScript Object Notation(JSON).

If the Blog “A” 304 had not already been crawled before, it is nowcrawled and its insights and URL are added to the Insights database 212.

The insights Insight 1 to Insight M of the Blog “A” 304 are thencorrelated in the Referred Traffic Analysis System 202 with relevantevents listed in the Referred Traffic listing 310 from the Web Analyticsmodule 108, including the fact that the User-i (308) who is reading theBlog “A” 304 has bought the product.

In a similar way, insights of other blogs, for example, are correlatedwith the referred traffic attributed to the respective other blogs togenerate a Referred Traffic Dynamics data set 312 stored in the RTDdatabase 218. The Referred Traffic Dynamics data set 312 is configuredto compute numeric correlations (totals) between specific insights suchas “awesome product” and actions such as “bought product”. A highrelative correlation between a specific insight (awesome product) andthe action (bought product) may lead to the plausible assumption thatthe insight has contributed to the user's decision to buy the product.

FIG. 4 is an example correlation overview diagram 400 to illustrate theoperation of the Referred Traffic Analysis System 202, showing: a numberof blog readers 402; a number of blogs 404 labeled “A” to “G”, each blogincluding a number of insights; a set of referred traffic events 406relating to each blog when the Client Website 110 is accessed throughthe respective blog; an Individual Referred Traffic Dynamics table 408;and a Referred Traffic Dynamics Summary table 412.

The blog readers 402 are grouped according to which blog they arereading. Readers which are merely visitors are shown as white circles,readers which ultimately purchase a product are shown as black circles.

The blogs 404, collected by the Web Crawler 206 (FIG. 2), are shown withsome insights they contain. The text of each blog may contain many moreinsights, not shown here because of space constraints. In any case, theinsights in each blog were identified by the Collector module 210 (FIG.2), which has matched the text of each blog against terms in theInsights Dictionary 222. These insights, along with the URL of each blogcontaining them, are stored in the Insights database 212.

The set of referred traffic events 406 is an example of the ReferredTraffic listing 310 (FIG. 3) that was obtained by the Web Analyticsmodule 108 which tracked the traffic activity at the Client Website 110.It was transmitted to the Referred Traffic Analysis System 202 andstored in the Analytics database 216 there. Each record of the set ofreferred traffic events 406 (referred to as events records) includes theURL of the referrer site, i.e., the blog, and a list of events at theClient Website 110 that pertained to, or originated from, the referrersite. For example, the first event record lists 5 visitors and 3 salesagainst the URL_A.

The Individual Referred Traffic Dynamics table 408 is computed bycombining information from the blog information 404 in the Insightsdatabase 212 with the 406 set of referred traffic events 406 in theAnalytics database 216 based on the blog URL as the key. The IndividualReferred Traffic Dynamics table 408 is divided into rows and columns,the rows being indexed with a primary key based on the name or URL of ablog, e.g., “A”, “B”, etc., and a secondary key based on the insights,e.g., “awesome product”, “nice packaging”, etc. in the present example.The columns are indexed by the primary and secondary keys, and by thetype of referred traffic event 410, such as “Visits” and “Sales”. It isnoted that “Visitors” (in the set of referred traffic events 406) and“Visits” (in the Individual Referred Traffic Dynamics table 408) areused interchangeably here, any distinction between repeat visits by thesame visitor may or may not be determined by the Web Analytics module108.

Only two types of referred traffic event 410 are shown in FIG. 4, butthere may be more types of referred traffic events 410 recorded by theWeb Analytics module 108 and the number of traffic event columns in theIndividual Referred Traffic Dynamics table 408 would correspond.

From the Individual Referred Traffic Dynamics table 408 it is thenpossible for example, that the blog “A” included 2 insights (“awesomeproduct” and “great taste”) and produced 5 visits and 3 sales.Similarly, the blog “C” included only one insight (“awesome product”)and produced 5 visits and 5 sales.

The data from which the Individual Referred Traffic Dynamics table 408is generated exists already, partially in the Analytics database 216 andpartially in the Insights database 212. Thus, the Individual ReferredTraffic Dynamics table 408 may be generated by the Associator module 214at any time, for example periodically at the times when a report needsto be sent to the client. For the purpose of recording a time history,and for the convenience in subsequent processing, it is convenient tostore this table in the RTD database 218 (FIG. 2) however.

The Referred Traffic Dynamics Summary table 412 is computed from theIndividual Referred Traffic Dynamics table 408 by omitting the blogidentifier and simply aggregating the results according to some apredefined scheme, for example by summing the entries of each types ofreferred traffic events column against the insights. In the presentexample, the result is: 31 visits and 12 sales for “awesome product”, 5visits and 3 sales against “great taste”, and 9 visits and 2 salesagainst “nice packaging”. Many conclusions may be drawn from such aresult, but one might conclude for example, that “awesome product” isthe most important insight since it led to the most visits as well assales, with 39% of visits leading to sales. On the other hand, theinsight “nice packaging” does not appear to be as powerful an insightsince it only led to 2 sales in 9 visits (22%). But even though theinsight “great taste” led to the fewest visits (5) it led to the highestproportion of sales (3, that is 60%). It is also possible to weight thetraffic measurement results from different blogs by a weighting factorassociated with respective referring blog posts to obtain a productbefore summing the products. Weighting factors may have been assigned toblogs according to a perceived influence of the blog, specific to theclient, for example blogs of product review sites may be assigned ahigher weighting factor than blogs from financial news sites.

The content of the RTD database 218 could be made directly available tothe client, but preferably the Presenter module 220 is used to provideaccess to only the client's data, formatting the data for interactiveaccess over the interactive Internet link 208, preprocessing, and thusreducing the amount of data that needs to be transmitted, and mostimportantly, providing the summarized data as described in the ReferredTraffic Dynamics Summary table 412 and derivations of it such as trendsetc.

In an alternative embodiment of the invention, the Insights database212, the Analytics database 216, and the RTD database 218 are combinedinto a single database stored in a computer readable medium.

FIG. 5 shows a flow chart 500 summarizing steps performed in theimproved system architecture 200 of FIG. 2, including steps:

502: Crawl Blog Post “A”;

504: Store URL of Blog Post “A”;

506: Extract Insights from “A”;

508: Retrieve Measurements for traffic from “A”;

510: Associate measurements with insights from “A”;

512: Aggregate measurements around unique insights; and

514: Present top insights to the client.

The step 502 (Crawl Blog Post “A”) is initiated by the Collector module210 and carried out by the Web Crawler 206. Blog Post “A” (Blog “A” 304of FIG. 3) was found by the Web Crawler 206 on the Internet, because ithas a reference (a button) pointing to the Client Website 110. Ingeneral, all blog posts, as well as other social media sites having areference to the Client Website 110, are referred to as “referring blogposts”, and may be crawled. In particular however, only a subset of thecrawled posts that are determined to be influential blog posts, may beevaluated in the Collector module 210. Certain methods for determininginfluential blog posts have been described in the earlier patentapplications of the same assignee cited above.

In the step 504 (Store URL of Blog Post “A”), the URL of the Blog Post“A” is stored by the Collector module 210 in the Insights database 212.

In the step 506 (Extract Insights from “A”), insights are extracted bythe Collector module 210 from the content of the Blog Post “A” andstored in the Insights database 212. Extraction is facilitated by theInsights Dictionary 222 of predefined insight tokens. While the insighttokens of the present embodiment of the invention are text elements suchas word and phrases, other types of insight tokens, for exampleemoticons, images, audio files, or other expressions used to indicate aninsight on a social media site, are also within the scope of theinvention.

In the step 508 (Retrieve Measurements for traffic from “A”), all webanalytics measurements are retrieved from the Web Analytics module 108for traffic to the Client Website 110 that was referred from the BlogPost “A”, or generally any traffic that was referred from any of therelevant crawled blog posts, i.e., blog posts that referred traffic tothe Client Website 110. The retrieved web analytics measurements arestored in the Analytics database 216 for use in the following step 510.

In the step 510 (Associate measurements with insights from “A”), theretrieved web analytics measurements are associated with the extractedinsights from the Blog Post “A”, or generally with the extractedinsights from all of the relevant blog posts and stored in theIndividual Referred Traffic Dynamics table 408 as described in detailabove.

In the step 512 (Aggregate measurements around unique insights), themeasurements around the unique insights identified and associated in theprevious steps, are aggregated into and the aggregated relations storedin the Referred Traffic Dynamics Summary table 412. The number ofrelevant blog posts may be on the order of thousands.

In the step 514 (Present top insights to the client), the top K (K=10,for example) insights that reoccur frequently enough to appear to be adriver for the measurements observed, are presented to the client. Inpractice, this means that a certain amount of manual intervention oriteration may be required to determine which insights and whichmeasurements are valuable to the client. The number K of top insightsmay vary, and may be conveniently selected to be in a range between 5and 20. It is understood that other values of K are also possible.

The entire process described in the flow chart 500 may be repeated foreach client periodically, for example daily or weekly. If retrieved webanalytics measurements (in the step 508) are found to contain a URL of areferring blog post, that had not been crawled and is not yet in theAnalytics database 216, a special crawl (Step 502) for that missing URLmay be initiated, in order to extract the insights of that additionalblog post (Steps 504 and 506) before the measurements may be associated(Step 510).

Thus, new methods and systems have been provided for improving theability to exploit the role of social networking sites in Internettraffic on commercial web sites, and in e-commerce in general.

Any and all software modules described in the present applicationcomprise a computer readable code stored in a computer readable storagemedium, for example memory, CD-ROM, DVD or the like, to be executed by aprocessor.

Although embodiments of the invention have been described in detail, itwill be apparent to one skilled in the art that variations andmodifications to the embodiments may be made within the followingclaims.

What is claimed is:
 1. A method of compiling marketing information for aclient, comprising: (a) crawling blog posts that contain at least onelink to a client website; (b) extracting an insight related to a topicfrom the crawled blog posts resulting in an extracted insight; (c) usinga web analytics tool to measure traffic referred to the client websiteby referring blog posts and to collect the traffic measurements oftraffic referred to the client website by the referring blog posts, thereferring blog posts including the crawled blog posts; and (d)associating the traffic measurements with the extracted insight.
 2. Themethod of claim 1, wherein the step (c) comprises: collecting one ormore events recorded in the client website.
 3. The method of claim 2,wherein the step (c) comprises: recording one or more of the followingevents: visits to the client website; number of orders of products fromthe client website; number of orders of products from the client websitedivided into a category; and number of downloads from the clientwebsite.
 4. The method of claim 1, further comprising: aggregating thetraffic measurements associated with the extracted insight across thecrawled blog posts.
 5. The method of claim 4, wherein the aggregatingcomprises: multiplying each traffic measurement by a weighting factorassociated with respective referring blog posts to obtain a product, andsumming the products.
 6. The method of claim 1, wherein the step (c)comprises: collecting the traffic measurements referred from a subset ofthe crawled blog posts.
 7. The method of claim 6, wherein the collectingcomprises: collecting the traffic measurements referred from the subsetof crawled blog posts, which are most influential blog posts.
 8. Themethod of claim 1, wherein the step (c) comprises: collecting thetraffic measurements referred from at least one additional blog post ofthe referring blog posts, the at least one additional blog post has notbeen crawled, the method further including: crawling the at least oneadditional blog post and extracting the insight from the at least oneadditional blog post.
 9. The method of claim 1, further comprising: (e)determining frequently reoccurring insights, which reoccur frequentlyenough to appear to be a driver for the measurements of traffic; and (f)presenting a plurality of said frequently reoccurring insights andassociated traffic measurements to the client.
 10. The method of claim9, wherein the determining comprises: determining the highest number ofinsights associated with one or more selected traffic measurements. 11.The method of claim 9, wherein the plurality of said frequentlyreoccurring insights includes a number (K) of most frequently occurringinsights, and K is between 5 and
 20. 12. A non-transitory computerreadable medium having computer readable instructions stored thereon forexecution by a processor, to perform steps of a method for compilingmarketing information for a client, comprising: (a) crawling blog poststhat contain at least one link to a client website; (b) extracting aninsight related to a topic from the crawled blog posts; (c) measuringtraffic referred to the client website by referring blog posts andcollecting the traffic measurements of traffic referred to the clientwebsite by the referring blog posts, the referring blog posts includingthe crawled blog posts; (d) associating the traffic measurements withthe extracted insight.
 13. A referred traffic analysis system forcompiling marketing information for a client, the system comprising aprocessor and a computer readable medium comprising a computer readableinstruction stored thereon for execution by the processor, forming: acrawler module to crawl blog posts that contain at least one link to aclient website, resulting in crawled blog posts; a collector module toextract an insight related to a topic from the crawled blog postsresulting in an extracted insight; a web analytics module to measuretraffic referred to the client website by referring blog posts and tocollect the traffic measurements of traffic referred to the clientwebsite by the referring blog posts, the referring blog posts includingthe crawled blog posts; and an associator module to associate thetraffic measurements with the extracted insight.
 14. The system of claim13, wherein the associator module comprises: an aggregator module toaggregate the traffic measurements associated with the extracted insightacross the crawled blog posts.
 15. The system of claim 13, wherein theaggregator module multiplies each of the traffic measurements by aweighting factor associated with respective referring blog posts toobtain a product, and then sums the products.
 16. The system of claim13, further comprising: a presenter module to determine frequentlyreoccurring insights that reoccur frequently enough to be a driver forthe traffic measurements; and to present a plurality of the frequentlyreoccurring insights and associated traffic measurement to the client.17. The system of claim 13, further comprising: an insights databasethat stores the extracted insights for each crawled blog.
 18. The systemof claim 17, wherein the collector module includes: an insightsdictionary that stores a list of predetermined insights terms fordetermining which insights to be extracted from each crawled blog post.19. The system of claim 13, further comprising: a referred trafficdynamics database that stores the traffic measurements associated withthe extracted one or more insights.
 20. The system of claim 16, whereinthe presenter module comprises: a graphic user interface module thatpresents the traffic measurements associated with the extracted one ormore insights.
 21. The system of claim 13, wherein the collector modulecollects the traffic measurements referred from at least one additionalblog post of the referring blog posts, the at least one additional blogpost has not been crawled, and wherein the crawler module crawls the atleast one additional blog post to extract the insight from the at leastone additional blog post.